In the past few years, with the rise of drone technology, several studies have been conducted that evaluate Structure from Motion (SfM) models based on their ability to render features in varying geomorphic landscapes. The majority of these studies, however, have taken place over very large areas and in very dramatic landscapes. Evaluations of SfMs in smoother landscapes and over less dramatic features, such as in the American Midwest, are less common. The purpose of this study is to evaluate the quality of SfMs based on error created when rendering the models based on different levels of processing accuracy in order to evaluate the efficiency of using drone imagery and SfMs to render small-scale geomorphic features within a Midwest American stream. I quantitatively compared three SfM models rendered at varying levels of processing accuracy over three individual stream channel study sites. I evaluated the different model’s accuracy in capturing channel features such as cut banks, point bars, woody debris, and stream channel width and depth. I compared the accuracy of the models in relation to the time taken for the models to render to evaluate their efficiency. Cross-section profiles across the stream channel in each SfM model were also quantitatively compared to show how well each model captures elevation changes across the study site. The results of this study ultimately show that SfMs offer a cheaper and more accessible alternative to LiDAR data for use in modeling Midwestern stream channels, but their accuracy and efficiency in modeling stream channel features is dependent on the characteristics of the study site.
|Commitee:||Zhou, Bin, Grossman, Michael|
|School:||Southern Illinois University at Edwardsville|
|School Location:||United States -- Illinois|
|Source:||MAI 81/12(E), Masters Abstracts International|
|Subjects:||Geomorphology, Physical geography, Geographic information science|
|Keywords:||Midwestern stream channels, Structure-from-motion, UAVs|
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